Summary: Improving Electric Load Forecasts
Using Network Committees
R. E. Abdel-Aal
Department of Physics,
King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Address for corresponding author:
Dr. R. E. Abdel-Aal
P. O. Box 1759
KFUPM
Dhahran 31261
Saudi Arabia
e-mail: radwan@kfupm.edu.sa
Phone: +966 3 860 4320
Fax: +966 3 860 4281
Abstract
Accurate daily peak load forecasts are important for secure and profitable operation of modern
power utilities, with deregulation and competition demanding ever-increasing accuracies.
Machine learning techniques including neural and abductive networks have been used for this
purpose. Network committees have been proposed for improving regression and classification
accuracy in many disciplines, but is yet to be widely applied to load forecasting. This paper

Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals

Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants